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There are numerous geo-climatic and human factors that contribute to theoccurrence of natural disasters in the real-world scenario. Besides the studyof causes and preconditions of such calamities, post-disaster analysis isessential for the efficient management of the disaster situation. This processneeds timely and accurate data in light of the increasing frequency andseverity of climate change-related extreme weather events. The analysis ofdisaster data involves the challenging task of integrating multipleheterogeneous sources, data ingestion and visualization. This paper aims atproviding a three-dimensional analytical view of disaster data as time-seriescharts and a statistical model to evaluate the correlation between theoccurrence of disasters, climate change and the corresponding economic damages(as a percentage of GDP). Therefore, statistical methodologies are leveraged toplay important role in managing disasters, from preparation to recovery andreporting. The graphical representations provide insights on regional trendsthat follow, related to factors such as the proportion of each type of disasterin the various losses incurred. Therefore, obtaining reliable information aboutthe population, the economy and climate are crucial both for risk managementand preparedness and for responding to disasters. The research puts forth adetailed statistical methodology with a spatial dimension to study the impactson the economy and infrastructure in the aftermath of a disaster therebyensuring specialized assistance. The inference of the analysis confirmed apositive correlation between climate change and occurrence of naturaldisasters. Therefore, statistical evidence of an important phenomenon likeclimate change affecting natural disasters brings awareness among thepopulation in the society to be more environmentally responsible.

A Correlation Analysis and Visualization of Climate Change using Post-Disaster Heterogeneous Datasets